Clinical research has recognized the importance of identifying and developing viable approaches to personalized treatment for MDD. The NIMH-funded multisite project Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) addressed this goal by exploiting an array of promising biomarkers for antidepressant treatment response, ranging from behavioral and resting EEG measures to task-related fMRI. For all sites, the Psychophysiology Laboratory at NYSPI implemented, certified and oversaw all EEG data acquisition, off-site processing and ongoing primary analyses. Data collection and treatment are completed, and analyses in all data domains are underway aimed at greater outcome prediction accuracy and specificity. Among the pre-treatment markers collected, the accuracy, relative ease of acquisition, and the cost-effectiveness of EEG measures make them particularly appealing and amenable to routine clinical use. Two putative resting EEG EMBARC biomarkers quantify oscillatory activity: posterior alpha current source density (CSD) and rostral anterior cingulate cortex theta current density (LORETA). These markers are separated by anatomy (posterior surface vs. deep anterior midline cortex), as well as by methodology, making their relationship to each other an unanswered question. The important and timely issue of functional connectivity between these markers and the rest of the brain is yet unknown, but holds significant promise for understanding the physiological mechanisms underlying their prediction of antidepressant treatment response. Since both biomarkers are based on reference-free current estimates, they provide superior measures of functional connectivity compared to those traditionally based on EEG potential differences at sensor level. Moreover, the combined use of CSD and principal components analysis (PCA), as advocated by our group, has been proven a reliable, generic, reference- and model- independent platform for the systematic and comprehensive representation of effective neuronal generator patterns at scalp, which in turn constrain and inform inferences about intracranial sources obtained by inverse models. This project will simplify and integrate the two EEG-based EMBARC biomarkers by using the project's subsample of 38 healthy controls for optimizing computation parameters for scalp-based CSD measures to enhance the consistency of the two methods (CSD, LORETA) in both frequency bands (alpha, theta). Common (coherence: [COH]; transfer function [TF]) and Granger causality (partial directed coherence [PDC]; directed transfer function [DTF]) connectivity measures will be developed and implemented within the CSD-PCA analysis platform. These developments will be subsequently applied to the full patient sample (n = 259) to probe corresponding differences in brain connectivity between patients who respond to SSRI (citalopram) or placebo and those who do not, thereby gaining important information for characterizing anatomical targets and connections critical for the treatment of, or resilience against, MDD.